Infrared Small Target Detection Under Complex Background

As an important topic in infrared(IR) search and tracking, a series of bottleneck problems and key techniques of IR small target detection in complex background are highly valued by the experts at home and abroad. This dissertation concentrates just in the two key techniques:the suppression of background clutter and the detection of the dim small moving targets in multi-frames.At first, this paper proposed two improved filter algorithm. Bilateral filtering, de-noising and effectively keeping the edge, is added a template which can protect small target with its filter operator; Regularizing filtering(RegAF) is adaptive to different structures.A integrated and complementary of RegAF and Robinson filtering is proposed by this dissertation. According to different scene, the simulations both show good robustness. And compared with the traditional methods the two have lower false alarm rate, higher gain of Signal-to-Clutter Ratio and Background Suppression Factor(BSF).In the image segmentation stage, an adaptive threshold value based on mean variance weighted information entropy is proposed with its parameters obtained by a lot of experimental data. With combination of characteristics of original image and filtered image, it is better able to remove the false target compared other segmentation algorithms.Next, maintenance and update the data connection of sequential images via kalman filter. During detection, track evaluation factors further eliminate false track, reduce the complexity of the calculation, and finally the real goal of the track is obtained.The results of whole algorithm are given in the end, which show that the proposed algorithm can detect right track of infrared dim-small target validly.